{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:52:28Z","timestamp":1783630348299,"version":"3.55.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2016,4,15]],"date-time":"2016-04-15T00:00:00Z","timestamp":1460678400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,4,15]],"date-time":"2016-04-15T00:00:00Z","timestamp":1460678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP140100087"],"award-info":[{"award-number":["DP140100087"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Asian Office of Aerospace Research and Development, Air Force Office of Scientic Research","award":["FA2386-15-1-4007"],"award-info":[{"award-number":["FA2386-15-1-4007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2016,7]]},"DOI":"10.1007\/s10618-015-0448-4","type":"journal-article","created":{"date-parts":[[2016,4,15]],"date-time":"2016-04-15T11:37:58Z","timestamp":1460720278000},"page":"964-994","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":425,"title":["Characterizing concept drift"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9963-5169","authenticated-orcid":false,"given":"Geoffrey I.","family":"Webb","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy","family":"Hyde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai Long","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5334-3574","authenticated-orcid":false,"given":"Francois","family":"Petitjean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,4,15]]},"reference":[{"key":"448_CR1","doi-asserted-by":"publisher","unstructured":"Aggarwal CC (2009) Data streams: an overview and scientific applications. Springer, Berlin, pp 377\u2013397. doi:\n                    10.1007\/978-3-642-02788-8_14","DOI":"10.1007\/978-3-642-02788-8_14"},{"key":"448_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: Proceedings of the 29th international conference on very large data bases, VLDB Endowment, 29:81\u201392","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"issue":"4","key":"448_CR3","first-page":"319","volume":"2","author":"D Angluin","year":"1988","unstructured":"Angluin D (1988) Queries and concept learning. Mach Learn 2(4):319\u2013342","journal-title":"Mach Learn"},{"key":"448_CR4","unstructured":"Babcock B, Datar M, Motwani R (2002) Sampling from a moving window over streaming data. In: Proceedings of the thirteenth annual ACM-SIAM symposium on discrete algorithms, Society for Industrial and Applied Mathematics, pp 633\u2013634"},{"key":"448_CR5","unstructured":"Baena-Garc\u0131a M, del Campo-\u00c1vila J, Fidalgo R, Bifet A, Gavalda R, Morales-Bueno R (2006) Early drift detection method. In: Fourth international workshop on knowledge discovery from data streams, 6:77\u201386"},{"issue":"2","key":"448_CR6","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1023\/A:1007604202679","volume":"41","author":"PL Bartlett","year":"2000","unstructured":"Bartlett PL, Ben-David S, Kulkarni SR (2000) Learning changing concepts by exploiting the structure of change. Mach Learn 41(2):153\u2013174","journal-title":"Mach Learn"},{"key":"448_CR7","doi-asserted-by":"crossref","unstructured":"Bifet A, Gama J, Pechenizkiy M, Zliobaite I (2011) Handling concept drift: importance, challenges and solutions. PAKDD-2011 Tutorial, Shenzhen, China","DOI":"10.1109\/CBMS.2010.6042653"},{"key":"448_CR8","doi-asserted-by":"crossref","unstructured":"Bifet A, Gavald\u00e0 R (2009) Adaptive learning from evolving data streams. In: Advances in intelligent data analysis VIII, Springer, 249\u2013260","DOI":"10.1007\/978-3-642-03915-7_22"},{"key":"448_CR9","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet A, Holmes G, Kirkby R, Pfahringer B (2010a) MOA: massive online analysis. J Mach Learn Res 11:1601\u20131604","journal-title":"J Mach Learn Res"},{"key":"448_CR10","doi-asserted-by":"crossref","unstructured":"Bifet A, Holmes G, Pfahringer B (2010b) Leveraging bagging for evolving data streams. In: Machine learning and knowledge discovery in databases, Springer, pp 135\u2013150","DOI":"10.1007\/978-3-642-15880-3_15"},{"key":"448_CR11","doi-asserted-by":"publisher","unstructured":"Bose RJC, van der Aalst WMP, Zliobaite I, Pechenizkiy M (2011) Handling concept drift in process mining. In: Haralambos M, Colette R (eds) Advanced information systems engineering., Lecture notes in computer science, Springer, Berlin, pp 391\u2013405. doi:\n                    10.1007\/978-3-642-21640-4_30","DOI":"10.1007\/978-3-642-21640-4_30"},{"issue":"1","key":"448_CR12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TNNLS.2013.2251352","volume":"25","author":"D Brzezinski","year":"2014","unstructured":"Brzezinski D (2014a) Reacting to different types of concept drift: the accuracy updated ensemble algorithm. Neural Netw Learn Syst IEEE Trans 25(1):81\u201394. doi:\n                    10.1109\/TNNLS.2013.2251352","journal-title":"Neural Netw Learn Syst IEEE Trans"},{"key":"448_CR13","unstructured":"Brzezi\u0144ski D (2010) Mining data streams with concept drift. Master\u2019s thesis, Poznan University of Technology"},{"issue":"1","key":"448_CR14","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TNNLS.2013.2251352","volume":"25","author":"D Brzezinski","year":"2014","unstructured":"Brzezinski D, Stefanowski J (2014b) Reacting to different types of concept drift: the accuracy updated ensemble algorithm. Neural Netw Learn Syst IEEE Trans 25(1):81\u201394","journal-title":"Neural Netw Learn Syst IEEE Trans"},{"key":"448_CR15","doi-asserted-by":"crossref","unstructured":"Brzezinski D, Stefanowski J (2014c) Prequential AUC for classifier evaluation and drift detection in evolving data streams. In: Proceedings of the 3rd international workshop on new frontiers in mining complex patterns, Nancy","DOI":"10.1007\/978-3-319-17876-9_6"},{"issue":"1","key":"448_CR16","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s10115-008-0139-1","volume":"18","author":"DA Cieslak","year":"2009","unstructured":"Cieslak DA, Chawla NV (2009) A framework for monitoring classifiers performance: when and why failure occurs? Knowl Inform Syst 18(1):83\u2013108 ISSN 0219-1377","journal-title":"Knowl Inform Syst"},{"key":"448_CR17","doi-asserted-by":"publisher","unstructured":"Dongre PB, Malik LG (2014) A review on real time data stream classification and adapting to various concept drift scenarios. In: Advance computing conference (IACC), 2014 IEEE international, pp 533\u2013537, doi:\n                    10.1109\/IAdCC.2014.6779381","DOI":"10.1109\/IAdCC.2014.6779381"},{"issue":"5\u20136","key":"448_CR18","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1002\/sam.10054","volume":"2","author":"Anton Dries","year":"2009","unstructured":"Dries Anton, R\u00fcckert Ulrich (2009) Adaptive concept drift detection. Stat Anal Data Min 2(5\u20136):311\u2013327","journal-title":"Stat Anal Data Min"},{"issue":"2","key":"448_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1145\/1083784.1083789","volume":"34","author":"Mohamed Medhat Gaber","year":"2005","unstructured":"Gaber Mohamed Medhat, Zaslavsky Arkady, Krishnaswamy Shonali (2005) Mining data streams: a review. ACM Sigmod Rec 34(2):18\u201326","journal-title":"ACM Sigmod Rec"},{"issue":"4","key":"448_CR20","doi-asserted-by":"publisher","first-page":"44:1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):44:1\u201344:37. doi:\n                    10.1145\/2523813\n                    \n                   ISSN 0360\u20130300","journal-title":"ACM Comput Surv"},{"key":"448_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01091-0_2","volume-title":"An overview on mining data streams, volume 206 of studies in computational intelligence","author":"J Gama","year":"2009","unstructured":"Gama J, Rodrigues P (2009) An overview on mining data streams, volume 206 of studies in computational intelligence. Springer, Berlin. doi:\n                    10.1007\/978-3-642-01091-0_2"},{"key":"448_CR22","doi-asserted-by":"crossref","unstructured":"Gama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In Ana LC, Bazzan, Sofiane L (ed), Advances in artificial intelligence SBIA","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"448_CR23","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-540-28645-5_29","volume-title":"Advances in artificial intelligence\u2014SBIA 2004","author":"J Gama","year":"2004","unstructured":"Gama J, Medas P, G Castillo, Rodrigues P (2004) Learning with drift detection. Advances in artificial intelligence\u2014SBIA 2004. Springer, New York, pp 286\u2013295"},{"key":"448_CR24","doi-asserted-by":"publisher","unstructured":"Gomes JB, Menasalvas E, Sousa PAC (2011) Learning recurring concepts from data streams with a context-aware ensemble. In: Proceedings of the 2011 ACM symposium on applied computing, SAC \u201911, ACM, New York, pp 994\u2013999. doi:\n                    10.1145\/1982185.1982403","DOI":"10.1145\/1982185.1982403"},{"key":"448_CR25","doi-asserted-by":"crossref","unstructured":"Hoens TR, Chawla NV, Polikar R (2011) Heuristic updatable weighted random subspaces for non-stationary environments. In Diane JC, Jian P, Wei W, Osmar RZ, Xindong W (ed), IEEE international conference on data mining, ICDM-11, IEEE, pp 241\u2013250","DOI":"10.1109\/ICDM.2011.75"},{"issue":"1","key":"448_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s13748-011-0008-0","volume":"1","author":"TR Hoens","year":"2012","unstructured":"Hoens TR, Polikar R, Chawla NV (2012) Learning from streaming data with concept drift and imbalance: an overview. Prog Artif Intell 1(1):89\u2013101. doi:\n                    10.1007\/s13748-011-0008-0","journal-title":"Prog Artif Intell"},{"key":"448_CR27","doi-asserted-by":"publisher","unstructured":"Huang DTJ, Koh YS, Gillian D, Pears R (2013) Tracking drift types in changing data streams. In: Hiroshi M, Wu Z, Cao L, Zaiane O, Min Y, Wei W (eds) Advanced data mining and applications. Lecture notes in computer science. Springer, Berlin, pp 72\u201383. doi:\n                    10.1007\/978-3-642-53914-5_7","DOI":"10.1007\/978-3-642-53914-5_7"},{"key":"448_CR28","doi-asserted-by":"crossref","unstructured":"Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD-01, ACM, pp 97\u2013106","DOI":"10.1145\/502512.502529"},{"issue":"1","key":"448_CR29","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/1121995.1121998","volume":"35","author":"N Jiang","year":"2006","unstructured":"Jiang N, Gruenwald L (2006) Research issues in data stream association rule mining. ACM SIGMOD Rec 35(1):14\u201319","journal-title":"ACM SIGMOD Rec"},{"key":"448_CR30","doi-asserted-by":"publisher","unstructured":"Kelly MG, Hand DJ, Adams NM (1999) The impact of changing populations on classifier performance. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, KDD-99, New York, ACM, pp 367\u2013371. doi:\n                    10.1145\/312129.312285","DOI":"10.1145\/312129.312285"},{"key":"448_CR31","unstructured":"Kosina Petr, Gama Jo\u00e3o, Sebasti\u00e3o Raquel (2010) Drift severity metric. European Conference on Artificial Intelligence, ECAI 2010:1119\u20131120"},{"key":"448_CR32","doi-asserted-by":"crossref","unstructured":"Krempl G, Zliobaite I, Brzezinski D, Hullermeier E, Last M, Lemaire V, Noack T, Shaker A, Sievi S, Spiliopoulou M, Stefanowski J (2014) Open challenges for data stream mining research. In: ACM SIGKDD explorations newsletter, vol 16\u20131, pp 1\u201310","DOI":"10.1145\/2674026.2674028"},{"key":"448_CR33","unstructured":"Kuh A, Petsche T, Rivest RL (1991) Learning time-varying concepts. In: Advances in neural information processing systems, pp 183\u2013189"},{"key":"448_CR34","doi-asserted-by":"crossref","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79\u201386","DOI":"10.1214\/aoms\/1177729694"},{"key":"448_CR35","doi-asserted-by":"crossref","unstructured":"Kuncheva LI (2004) Classifier ensembles for changing environments. In: Multiple Classifier Systems. Springer, pp 1\u201315","DOI":"10.1007\/978-3-540-25966-4_1"},{"issue":"6","key":"448_CR36","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1109\/TKDE.2010.61","volume":"23","author":"MM Masud","year":"2011","unstructured":"Masud MM, Gao J, Khan L, Han J, Thuraisingham B (2011) Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans Knowl Data Eng 23(6):859\u2013874","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"448_CR37","volume-title":"A theory and methodology of inductive learning","author":"RS Michalski","year":"1983","unstructured":"Michalski RS (1983) A theory and methodology of inductive learning. Springer, New York"},{"key":"448_CR38","unstructured":"Minku FL, Yao X (2009) Using diversity to handle concept drift in on-line learning. In: International joint conference on neural networks, IJCNN-09, IEEE, pp 2125\u20132132"},{"issue":"5","key":"448_CR39","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1109\/TKDE.2009.156","volume":"22","author":"LL Minku","year":"2010","unstructured":"Minku LL, White AP, Xin Y (2010) The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans Knowl Data Eng 22(5):730\u2013742. doi:\n                    10.1109\/TKDE.2009.156\n                    \n                   ISSN 1041\u20134347","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"448_CR40","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","volume":"45","author":"Jose G Moreno-Torres","year":"2012","unstructured":"Moreno-Torres Jose G, Raeder Troy, Alaiz-Rodrguez Rocio, Chawla Nitesh\u00a0V, Herrera Francisco (2012) A unifying view on dataset shift in classification. Pattern Recognit 45(1):521\u2013530 ISSN 0031-3203","journal-title":"Pattern Recognit"},{"key":"448_CR41","unstructured":"Narasimhamurthy A, Kuncheva L (2007) A framework for generating data to simulate changing environments. In: Proceedings of the 25th IASTED international multi-conference: artificial intelligence and applications, ACTA Press, 549: p 389"},{"key":"448_CR42","unstructured":"Nguyen H-L, Woon Y-K, Ng W-K, Wan L (2012) Heterogeneous ensemble for feature drifts in data streams. In: Advances in knowledge discovery and data mining. Springer, pp 1\u201312"},{"key":"448_CR43","unstructured":"Nguyen H-L, Woon Y-K, Ng W-K (2014) A survey on data stream clustering and classification. Knowl Inf Syst pp 1\u201335"},{"key":"448_CR44","unstructured":"Nishida Kyosuke, Yamauchi K (2007) Detecting concept drift using statistical testing. In: Discovery Science, Springer, pp 264\u2013269"},{"key":"448_CR45","unstructured":"Oza NC, Russell S (2001) Online bagging and boosting. In: Artificial Intelligence and Statistics 2001, Morgan Kaufmann pp 105\u2013112"},{"key":"448_CR46","doi-asserted-by":"publisher","unstructured":"Pfahringer B, Holmes G, Kirkby R (2007) New options for Hoeffding trees. In: Mehmet O, John T (eds) AI 2007: advances in artificial intelligence, 4830th edn., Lecture notes in computer scienceSpringer, New York, pp 90\u201399. doi:\n                    10.1007\/978-3-540-76928-6_11","DOI":"10.1007\/978-3-540-76928-6_11"},{"key":"448_CR47","volume-title":"Dataset shift in machine learning","author":"J Quionero-Candela","year":"2009","unstructured":"Quionero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND (2009) Dataset shift in machine learning. The MIT Press, Cambridge"},{"key":"448_CR48","doi-asserted-by":"crossref","unstructured":"Shaker A, Hullermeier E (2015) Recovery analysis for adaptive learning from non-stationary data streams. In: Neurocomputing, ScienceDirect, pp 250\u2013264","DOI":"10.1016\/j.neucom.2014.09.076"},{"key":"448_CR49","unstructured":"Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2006) Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd international conference on very large data bases, VLDB Endowment, pp 187\u2013198"},{"key":"448_CR50","unstructured":"Tsymbal A (2004) The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15, The University of Dublin, Trinity College, Department of Computer Science, Dublin"},{"key":"448_CR51","doi-asserted-by":"crossref","unstructured":"Wetzel L (2009) Types and tokens. In: Zalta EN (ed) The Stanford Encyclopedia of Philosophy. \n                    http:\/\/plato.stanford.edu\/archives\/spr2014\/entries\/types-tokens\/","DOI":"10.7551\/mitpress\/9780262013017.001.0001"},{"key":"448_CR52","doi-asserted-by":"publisher","unstructured":"Wang H, Fan W, Yu PS, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD-03, New York, ACM, pp 226\u2013235. doi:\n                    10.1145\/956750.956778","DOI":"10.1145\/956750.956778"},{"key":"448_CR53","doi-asserted-by":"crossref","unstructured":"Wang H, Fan W, Yu PS, Han J (2003b) Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD-03, ACM, pp 226\u2013235","DOI":"10.1145\/956750.956778"},{"key":"448_CR54","doi-asserted-by":"crossref","unstructured":"Wang S, Minku LL, Ghezzi D, Caltabiano D, Tino P, Yao X (2013) Concept drift detection for online class imbalance learning. In: The 2013 international joint conference on neural Network, IJCNN-13, IEEE, pp 1\u201310","DOI":"10.1109\/IJCNN.2013.6706768"},{"issue":"1","key":"448_CR55","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/BF00116900","volume":"23","author":"G Widmer","year":"1996","unstructured":"Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69\u2013101. doi:\n                    10.1007\/BF00116900\n                    \n                   ISSN 0885\u20136125","journal-title":"Mach Learn"},{"key":"448_CR56","doi-asserted-by":"publisher","unstructured":"Zhang P, Zhu X, Shi Y (2008) Categorizing and mining concept drifting data streams. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD-08, ACM, pp 812\u2013820. doi:\n                    10.1145\/1401890.1401987","DOI":"10.1145\/1401890.1401987"},{"key":"448_CR57","unstructured":"Zliobaite I (2010) Learning under concept drift: an overview. Technical report"},{"key":"448_CR58","first-page":"565","volume-title":"Knowledge and information systems","author":"I Zliobaite","year":"2014","unstructured":"Zliobaite I (2014) Controlled permutation for testing adaptive learning models. Knowledge and information systems, vol 39. Springer, London, pp 565\u2013578"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-015-0448-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-015-0448-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-015-0448-4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-015-0448-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,17]],"date-time":"2020-05-17T13:45:11Z","timestamp":1589723111000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-015-0448-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,15]]},"references-count":58,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,7]]}},"alternative-id":["448"],"URL":"https:\/\/doi.org\/10.1007\/s10618-015-0448-4","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,15]]},"assertion":[{"value":"1 March 2015","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2015","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}